چکیده انگلیسی

Benchmarking of container terminals is an important issue facing port management. Data envelopment analysis (DEA), which is a multi-factor productivity measurement tool is generally used in assessing the relative efficiency of homogenous units and setting benchmark for inefficient units. Evaluation of container terminals by DEA produces limited set of efficient units which are reference to inefficient units irrespective of their differences in efficiency scores. DEA projects the lowest efficient units to highest efficient units but in reality, the reference set may be very different in size, environment and operating practices. Every container terminal is characterized by some physical values that represent relevant properties of the terminal. DEA, if employed alone, to measure the efficiency and set benchmark for inefficient terminals to improve efficiency may give biased result because all container terminals vary in their capacity. In order to overcome this shortcoming, in this paper, data mining and DEA are fused to provide a diagnostic tool to effectively measure the efficiency of inefficient terminals and prescribe a step-wise projection to reach the frontier in accordance with their maximum capacity and similar input properties which otherwise is not possible with DEA alone.

مقدمه انگلیسی

The container terminal is the physical link between ocean and land modes of transport and a major component of containerization system (Dowd & Leschine, 1990). In order to support trade oriented economic development, port authorities have increasingly been under pressure to improve port efficiency by ensuring that port services are provided on an increasingly competitive basis. Ports form a vital link in the overall trading chain and, consequently, port efficiency is an important contributor to a nation’s international competitiveness (Chin and Tongzon, 1998 and Tongzon, 1989). There are various papers based on efficiency measurement of container port industry in relation to productive activities. In particular, non-parametric frontier methods data envelopment analysis (DEA) has been developed with application across a wide range of sectors. The applications of standard DEA models such as the Charnes, Cooper and Rhodes (CCR) (Charnes, Cooper, & Rhodes, 1978) and Banker, Charnes and Rhodes (BCC) (Banker, Charnes, & Cooper, 1984) have been applied to container port industry to measure the efficiency. Other methods that have been used in efficiency measurement of ports are DEA windows analysis, stochastic cost frontier (SCF) and stochastic production frontier method (SPF).
Although benchmarking in DEA allows for the identification of targets for improvements, it has certain limitations. A drawback in benchmarking is that inefficient DMUs are projected to the efficient frontier ignoring the differences in the efficiency score. Another drawback regarding the use of DEA model is that an inefficient DMU and its benchmarks may not be inherently similar in the operating practices (Doyle & Green, 1994). This paper enhances the capability of DEA by using DEA recursive analysis to provide reference set to inefficient units in accordance with their maximum capacity to improve efficiency to their optimal level in contrast to an unrealistic level. Next clustering is carried out using unsupervised clustering tool Kohonen’s self-organizing map (KSOM) (Kohonen, 1982) to cluster units with similar input properties so as to make appropriate benchmarking at the respective stratified efficiency levels obtained by using DEA recursive analysis.
The rest of the paper is organized as follows: In Section 2 there is a review of literature on DEA and efficiency measures in port sector. Section 3 deals with the limitations of DEA and proposes a methodology to overcome these shortcomings. Section 4 deals with the practical application, the results of which are displayed in the tables accordingly. The final section discusses the conclusion and future research issues.

نتیجه گیری انگلیسی

DEA as a multi-factor productivity measurement model is used to measure the efficiency and set benchmarks for the inefficient terminals to improve efficiency. But the benchmark that is derived to improve inefficient units by traditional application of DEA may give bias results. Furthermore they may not be inherently similar in the physical and operational characteristics. To overcome this problem, in this paper, two important fields of information technology DEA and data mining were integrated to achieve a synergy result that could not have been possible if each model were to operate individually.
The benchmarking and improvement projection using the conventional DEA procedure is not desirable because the inefficient DMUs are projected to the efficient frontier ignoring the differences in the efficiency score. Upon analysis it was found that the efficiency score of DMUs ranged from 4.75% to 100% out of which 13 container terminals are found to be efficient with a score of 1. The 57 inefficient terminals had to refer these limited efficient terminals for improvement. In general, the benchmarking is done to improve the performance of terminals. But a terminal with low score of 4.75% cannot make direct improvement projections to 100%, it needs a terminal with a reasonably equivalent characteristics and capacity for benchmarking and improvement. In this paper we have achieved this goal by partitioning the container terminals into multiple efficient levels utilizing recursive DEA analysis. The DEA recursive analysis provided a step-wise reference path. The recursive analysis produced four tiers enabling the terminals in respective tiers choose targets to their immediate upper tier. The use of DEA followed by clustering tool SOM will allow manager to accurately decide in which efficient level their enterprise or container terminal falls and their capacity to maximize efficiency to a certain level based on their resources. Thus in contrast to classical DEA, where all the inefficient terminals are projected to a set of ‘best-practice’ frontier without any scope for level-wise improvement, our methodology provides a ladder for inefficient terminals to reach the frontier in a step-wise manner in accordance with their maximum capacity and similar input properties (obtained by using SOM) to reach an optimal level rather than an unrealistic level.
Thus we used multi-factor efficiency model, modified the model to fit into our research objective and fused it with data mining tool, SOM, for evaluating container terminal performance and in identifying appropriate benchmarks for improving poorly performing container terminals thus taking advantage of each methodologies specific characteristics in solving the problems which otherwise is not possible using DEA alone. Managers and policy makers for improving the operational efficiencies of container terminals through benchmarking can utilize the dual methods in this paper.
As an extension of this study, investigating and evaluating various clustering approaches, factor analysis, and multi-dimensional scaling can all be avenues for further research. Incorporating managerial preferences could also be an approach to investigate the DEA and clustering results. Decomposing container terminal operation processes and investigating those processes for benchmarking purposes may also provide ample insight for container terminal operations managers. Additionally, a qualitative analysis can be integrated with the quantitative analysis – DEA analysis to relate ‘best practices’, as perceived by managers, with DEA results.